Data Science Africa summer school is aimed at equipping participants with Machine Leaning, Data Science and Artificial Intelligence skills. This will be organised in sesions and after each session there will be exercises to assess the learning. To this end, we require that participants are well versed with the basics of the technologies and languages that will be used in the summer school. Particularly, we want to make sure participants have sufficient base skills in Python programming, Data science and Machine learning. You are required to download the notebook from the link below (by clicking it), complete the notebook and then fill out the registration form below that requires you to upload the completed notebook.

The U.S. Child Welfare System (CWS) is charged with improving outcomes for foster youth; yet, they are overburdened and underfunded. To overcome this limitation, several states have turned towards algorithmic decision-making systems to reduce costs and determine better processes for improving CWS outcomes. Using a human-centered algorithmic design approach, we synthesize 50 peer-reviewed publications on computational systems used in CWS to assess how they were being developed, common characteristics of predictors used, as well as the target outcomes. We found that most of the literature has focused on risk assessment models but does not consider theoretical approaches (e.g., child-foster parent matching) nor the perspectives of caseworkers (e.g., case notes). Therefore, future algorithms should strive to be context-aware and theoretically robust by incorporating salient factors identified by past research. We provide the HCI community with research avenues for developing human-centered algorithms that redirect attention towards more equitable outcomes for CWS.

With the advancements in computing technology and web-based applications, data is increasingly generated in multi-dimensional form. This data is usually sparse due to the presence of a large number of users and fewer user interactions. To deal with this, the Nonnegative Tensor Factorization (NTF) based methods have been widely used. However existing factorization algorithms are not suitable to process in all three conditions of size, density, and rank of the tensor. Consequently, their applicability becomes limited. In this paper, we propose a novel fast and efficient NTF algorithm using the element selection approach. We calculate the element importance using Lipschitz continuity and propose a saturation point based element selection method that chooses a set of elements column-wise for updating to solve the optimization problem. Empirical analysis reveals that the proposed algorithm is scalable in terms of tensor size, density, and rank in comparison to the relevant state-of-the-art algorithms.

Last week the daughter of the president of Russia, Vladimir Putin, Katerina Tikhonova, was appointed to head the Artificial Intelligence (AI) Institute located at Moscow State University. The university has produced 13 Nobel prizes, six Fields Medals and one Turing award, so in matters of science, putting the AI institute there is a big deal. In Russian, if a husband's last name is, for instance, Komlev, the wife's surname becomes Komleva. Thinking algorithmically, you add an "a" at the end of the husband's or the father's last name to get the wife's or the daughter's last name. So Katerina's surname is Tikhonova, which means that her husband's or one of her paternal ancestor's last name was Tikhonov.

Coupled Matrix Tensor Factorization (CMTF) facilitates the integration and analysis of multiple data sources and helps discover meaningful information. Nonnegative CMTF (N-CMTF) has been employed in many applications for identifying latent patterns, prediction, and recommendation. However, due to the added complexity with coupling between tensor and matrix data, existing N-CMTF algorithms exhibit poor computation efficiency. In this paper, a computationally efficient N-CMTF factorization algorithm is presented based on the column-wise element selection, preventing frequent gradient updates. Theoretical and empirical analyses show that the proposed N-CMTF factorization algorithm is not only more accurate but also more computationally efficient than existing algorithms in approximating the tensor as well as in identifying the underlying nature of factors.

As machine learning algorithms are increasingly deployed for high-impact automated decision making, ethical and increasingly also legal standards demand that they treat all individuals fairly, without discrimination based on their age, gender, race or other sensitive traits. In recent years much progress has been made on ensuring fairness and reducing bias in standard machine learning settings. Yet, for network embedding, with applications in vulnerable domains ranging from social network analysis to recommender systems, current options remain limited both in number and performance. We thus propose DeBayes: a conceptually elegant Bayesian method that is capable of learning debiased embeddings by using a biased prior. Our experiments show that these representations can then be used to perform link prediction that is significantly more fair in terms of popular metrics such as demographic parity and equalized opportunity.

Microsoft is looking for entrepreneurs to join its latest programme. Specifically social entrepreneurs as part of its recently launched Global Social Entrepreneurship programme, which has been made available in 140 countries, including South Africa. The programme forms part of the Microsoft for Startups initiative and aims to give entrepreneurs access to the necessary technology they need in order to get their socially-focused projects running. "The Global Social Entrepreneurship programme has benefits aimed specifically at elevating startups addressing an important social and/or environmental challenge through their products, services or operations," Microsoft explained regarding the announcement. "Solving global social and environmental challenges requires synergy of the right technology, partners, conducive environment and technology. When startups work together with investors, enterprises, governments, non-profits and communities, we are able to unlock new potentials," adds Microsoft4Afrika director, Amrote Abdella.

In the service of business, of society at large, artificial intelligence (AI) can be effective. Can it also be ethical? The wisdom of crowds, gleaned from social media, can paint a gestalt picture of how a government agency's, bank's or retailer's efforts are being received on the ground, so to speak. And it can also (perhaps), fed through models and analytics, can bolster decision-making for the greater, common good. Public opinion matters, after all, but across the social media platforms, the chatrooms -- the chatbots, even -- making sense of qualitative data is a challenge for most enterprises.

Artificial intelligence (AI) is "completely living up to its expected hype and hysteria," with 70% of daily digital interactions being AI-based, and businesses generating multimillion-dollar revenue streams from it. This was the word from Mike Bugembe, founder of UK-based AI consultancy, Lens.ai, delivering a keynote at the ITWeb Business Intelligence Summit 2020, in Johannesburg, today. Bugembe is a bestselling author, international speaker and executive advisor, helping organisations use data and AI to transform their businesses and grow. Discussing the importance of an AI strategy to gain business value, Bugembe noted that companies across the globe are ramping up investments in AI-related technologies and gaining multimillion-dollar-revenue streams, cutting costs, managing risk, improving operations, and finding innovative ways to develop products and strengthen customer intimacy. However, he warned that without an intelligent roadmap, companies risk focusing on the wrong opportunities, resulting in failure to tap into the true promise of AI. "Business and technology experts believe AI will be the most significant technological revolution that businesses have ever experienced," said Bugembe.

BLISS Team Educational Services, Bred Hub, has called on the Federal Government to introduce Artificial Intelligence in school curriculum as part of measures to prepare this generation for the future. Speaking ahead of train-the-trainer programme in Lagos, the General Manager, Bliss Team Educational Services, Christian Chime, said that government must champion the initiative that would introduce children and youths to the world of innovation through the teaching of Robotics, Artificial Intelligence, Coding and Science, Technology, Engineering and Mathematics (STEM) education. Chime noted that in the nearest future, the world would expect AI/Robotics to be a way of life and would play great roles in human existence on earth; adding: "Whoever leads in Artificial intelligence in 2030 will rule the world until 2100. We want this skill to be part of school curriculum, we want every student to be able to build and programme robotics. Irrespective of what a child wants to become, he or she needs to understand how to use technology because it is taking over every industry it makes process faster in whatever industry they are going to find themselves. "Bred Hub in partnership with UBTECH is organizing an interactive Artificial Intelligence and Robotics training for teachers and educators so that they can educate their students.